The aim of this study was to evaluate various mathematical
methods for enhanced parameter estimation of bi-exponential DWI
(12 b values 0-2000 s/mm2) of prostate cancer. Least Squares (LSQ),
Bayesian Shrinkage (BS) and Maximum Penalized Likelihood Estimation (MPLE) fitting
methods were evaluated in the terms of Coefficients of Variation (CV), Contrast
to Noise Ratio (CNR) and the Area under the curve (AUC) between tumor and
non-tumor prostate tissue. BS and MPLE methods improved AUC and CNR values of
bi-exponential model parameters and also decreased CV values in comparison with
the commonly used LSQ fitting method.

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